Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | The Semantic Web |
| Untertitel | 22nd European Semantic Web Conference, ESWC 2025, Proceedings |
| Herausgeber/-innen | Edward Curry, Maribel Acosta, Maria Poveda-Villalón, Marieke van Erp, Adegboyega Ojo, Katja Hose, Cogan Shimizu, Pasquale Lisena |
| Herausgeber (Verlag) | Springer Science and Business Media Deutschland GmbH |
| Seiten | 174-191 |
| Seitenumfang | 18 |
| ISBN (elektronisch) | 978-3-031-94578-6 |
| ISBN (Print) | 9783031945779 |
| Publikationsstatus | Veröffentlicht - 31 Mai 2025 |
| Veranstaltung | 22nd European Semantic Web Conference, ESWC 2025 - Portoroz, Slowenien Dauer: 1 Juni 2025 → 5 Juni 2025 |
Publikationsreihe
| Name | Lecture Notes in Computer Science |
|---|---|
| Band | 15719 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (elektronisch) | 1611-3349 |
Abstract
Ontology Alignment (OA) is fundamental for achieving semantic interoperability across diverse knowledge systems. We present OntoAligner, a comprehensive, modular, and robust Python toolkit for ontology alignment, designed to address current limitations with existing tools faced by practitioners. Existing tools are limited in scalability, modularity, and ease of integration with recent AI advances. OntoAligner provides a flexible architecture integrating existing lightweight OA techniques such as fuzzy matching but goes beyond by supporting contemporary methods with retrieval-augmented generation and large language models for OA. The framework prioritizes extensibility, enabling researchers to integrate custom alignment algorithms and datasets. This paper details the design principles, architecture, and implementation of the OntoAligner, demonstrating its utility through benchmarks on standard OA tasks. Our evaluation highlights OntoAligner’s ability to handle large-scale ontologies efficiently with few lines of code while delivering high alignment quality. By making OntoAligner open-source, we aim to provide a resource that fosters innovation and collaboration within the OA community, empowering researchers and practitioners with a toolkit for reproducible OA research and real-world applications.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Theoretische Informatik
- Informatik (insg.)
- Allgemeine Computerwissenschaft
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The Semantic Web : 22nd European Semantic Web Conference, ESWC 2025, Proceedings. Hrsg. / Edward Curry; Maribel Acosta; Maria Poveda-Villalón; Marieke van Erp; Adegboyega Ojo; Katja Hose; Cogan Shimizu; Pasquale Lisena. Springer Science and Business Media Deutschland GmbH, 2025. S. 174-191 (Lecture Notes in Computer Science; Band 15719 LNCS).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - OntoAligner
T2 - 22nd European Semantic Web Conference, ESWC 2025
AU - Babaei Giglou, Hamed
AU - D’Souza, Jennifer
AU - Karras, Oliver
AU - Auer, Sören
N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025/5/31
Y1 - 2025/5/31
N2 - Ontology Alignment (OA) is fundamental for achieving semantic interoperability across diverse knowledge systems. We present OntoAligner, a comprehensive, modular, and robust Python toolkit for ontology alignment, designed to address current limitations with existing tools faced by practitioners. Existing tools are limited in scalability, modularity, and ease of integration with recent AI advances. OntoAligner provides a flexible architecture integrating existing lightweight OA techniques such as fuzzy matching but goes beyond by supporting contemporary methods with retrieval-augmented generation and large language models for OA. The framework prioritizes extensibility, enabling researchers to integrate custom alignment algorithms and datasets. This paper details the design principles, architecture, and implementation of the OntoAligner, demonstrating its utility through benchmarks on standard OA tasks. Our evaluation highlights OntoAligner’s ability to handle large-scale ontologies efficiently with few lines of code while delivering high alignment quality. By making OntoAligner open-source, we aim to provide a resource that fosters innovation and collaboration within the OA community, empowering researchers and practitioners with a toolkit for reproducible OA research and real-world applications.
AB - Ontology Alignment (OA) is fundamental for achieving semantic interoperability across diverse knowledge systems. We present OntoAligner, a comprehensive, modular, and robust Python toolkit for ontology alignment, designed to address current limitations with existing tools faced by practitioners. Existing tools are limited in scalability, modularity, and ease of integration with recent AI advances. OntoAligner provides a flexible architecture integrating existing lightweight OA techniques such as fuzzy matching but goes beyond by supporting contemporary methods with retrieval-augmented generation and large language models for OA. The framework prioritizes extensibility, enabling researchers to integrate custom alignment algorithms and datasets. This paper details the design principles, architecture, and implementation of the OntoAligner, demonstrating its utility through benchmarks on standard OA tasks. Our evaluation highlights OntoAligner’s ability to handle large-scale ontologies efficiently with few lines of code while delivering high alignment quality. By making OntoAligner open-source, we aim to provide a resource that fosters innovation and collaboration within the OA community, empowering researchers and practitioners with a toolkit for reproducible OA research and real-world applications.
KW - Large Language Models
KW - Ontology Alignment
KW - Ontology Matching
KW - Python Library
KW - Retrieval Augmented Generation
UR - http://www.scopus.com/inward/record.url?scp=105007815364&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-94578-6_10
DO - 10.1007/978-3-031-94578-6_10
M3 - Conference contribution
AN - SCOPUS:105007815364
SN - 9783031945779
T3 - Lecture Notes in Computer Science
SP - 174
EP - 191
BT - The Semantic Web
A2 - Curry, Edward
A2 - Acosta, Maribel
A2 - Poveda-Villalón, Maria
A2 - van Erp, Marieke
A2 - Ojo, Adegboyega
A2 - Hose, Katja
A2 - Shimizu, Cogan
A2 - Lisena, Pasquale
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 1 June 2025 through 5 June 2025
ER -